SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides | Zendy

Lida Zhang | Zendy; Yuyi Huang | Zendy; Zehong Zou | Zendy; Ying He | Zendy; Chen Xi-mo | Zendy; Ailin Tao | Zendy

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SORTALLER: predicting allergens using substantially optimized algorithm on allergen family featured peptides

Author(s) -

Lida Zhang,

Yuyi Huang,

Zehong Zou,

Ying He,

Chen Xi-mo,

Ailin Tao

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts326

Subject(s) - allergen , computer science , support vector machine , software , algorithm , sensitivity (control systems) , classifier (uml) , artificial intelligence , allergy , biology , immunology , programming language , engineering , electronic engineering

SORTALLER is an online allergen classifier based on allergen family featured peptide (AFFP) dataset and normalized BLAST E-values, which establish the featured vectors for support vector machine (SVM). AFFPs are allergen-specific peptides panned from irredundant allergens and harbor perfect information with noise fragments eliminated because of their similarity to non-allergens. SORTALLER performed significantly better than other existing software and reached a perfect balance with high specificity (98.4%) and sensitivity (98.6%) for discriminating allergenic proteins from several independent datasets of protein sequences of diverse sources, also highlighting with the Matthews correlation coefficient (MCC) as high as 0.970, fast running speed and rapidly predicting a batch of amino acid sequences with a single click.

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